1. Field of the Invention
The present invention relates generally to a sensor, and particularly to a sensor with reduced power use.
2. Technical Background
The rapidly growing demands for wireless sensing applications have introduced an urgent need for high complexity, power aware mixed-signal circuits. Integrating analog signal acquisition units (amplification, filtering, and analog-to-digital conversion), digital logic for signal processing and control, and RF capabilities into a single monolithic circuit results in the anticipated advantages of more efficient power management and energy allocation, lower cost, and smaller form factor. Due to the high degree of integration possible, modern CMOS technologies accommodate system-on-a-chip solutions for many applications; among them smart-sensor units (SSU's), where all required subsystems, both analog and digital, may be integrated into a single mixed-signal CMOS chip.
One possible specific realization of a SSU for use with a piezoelectric sensor is shown in FIG. 1. A typical unit 100 would include a sensing element 102, an analog signal processing unit, e.g., a charge preamplifier 104 and analog-to-digital converter (ADC) 106, control logic such as a microprocessor 108 with memory 110, a digital signal processing (DSP) unit 112 with memory 114, and a communication (COMM) module 116. The charge preamplifier amplifies the sensor signal to the level that matches the full dynamic range of the ADC. The ADC then converts the amplified analog signal to a digital format compatible with the digital logic circuits. Both the charge preamplifier and ADC work under the control of the micro-controller, which controls the amplifier gain and the ADC's resolution and bandwidth. The ADC may be either a Nyquist ADC, in which multi-bit samples are obtained at a rate equal to twice the signal bandwidth, or an oversampling sigma-delta ADC, in which low-bit samples are obtained at a much higher rate and subsequently decimated to high-resolution samples at a lower rate. The data are processed in the DSP unit which typically may perform filtering, spectrum analysis, correlation, statistical analysis (e.g. histogram calculation), and data compression if required for subsequent communications.
In modern designs, the micro-controller and DSP units are fused into a single control/signal-processing unit. The last element of the system is a communication unit (COMM) that connects the smart-sensor to the external world via wired or wireless links. The front-end of the Smart Sensor Unit, including the charge preamplifier and ADC, is in many ways the most critical part of the system. It determines the system's overall dynamic range and bandwidth, and furthermore, in sensor monitoring applications, the front end must be “on” nearly all of the time so it is the primary determinant of the overall system energy consumption.
Throughout the last two decades a great deal of research and development has been dedicated to analog-to-digital converters. There are two basic ADC types, Nyquist rate and oversampling. Oversampling ADC's based on sigma-delta (ΣΔ) modulation have been employed in high-resolution and low to moderate bandwidth signal acquisition for such applications as instrumentation and biomedical measurements, digital audio, and ISDN. Nyquist rate ADC's have been used for low to moderate resolution and high-bandwidth applications. Although Nyquist rate converters may achieve higher bandwidth, over-sampling ADC's have several distinct advantages. Specifically, ΣΔ ADC's simplify integration by reducing the demands on the supporting analog circuits. For example, sharp roll-off anti-aliasing filters or highly precise sample-and-hold circuits are not required. Furthermore, over-sampling ADC's are relatively tolerant of circuit non-idealities and component mismatch and therefore do not require post-fabrication trimming or calibration to achieve high resolution. The scaling of modern VLSI technologies, with the drive toward smaller and faster (but less precise) components to improve digital circuit performance, has created an opportunity for ΣΔ converters. Fundamentally, oversampling ADC's derive their performance from the speed of their circuit components, while Nyquist rate converters derive their performance from the precision of their circuit components, thus the oversampling ADC design approach is compatible with the trend in CMOS technology.
Another important advantage of ΣΔ ADC's is that they generally require lower power consumption for a given resolution and bandwidth than Nyquist ADC's. Furthermore, the high dynamic range of ΣΔ ADC's relaxes the requirements on the charge pre-amplifier, i.e., the gain does not have to be carefully matched to the ADC input range. Finally, switched-capacitor circuit technology, which is almost always used for ΣΔ ADC's, allows the ΣΔ ADC to be connected directly to piezoelectric elements, completely eliminating the need for impedance matching amplifiers. Thus the ΣΔ ADC architecture creates an opportunity to optimize the system by eliminating the charge-preamplifier.
An example of the optimized conventional structure is shown in FIG. 2 as 200. A comparison with FIG. 1 shows that the charge preamplifier 104 and the ADC 106 have been replaced with a ΣΔ ADC 206.
In most smart-sensing applications important events that require a full deployment of the network resources and end-user attention are generally infrequent. As a consequence, the SSU spends the vast majority of time monitoring an object during times of relative inactivity. During these periods of inactivity, the input signal from the transducer is not information bearing, and the SSU consumes its valuable energy resources to monitor such input signals seeking the onset of the information carrying signal part. Since the onset of the information carrying input signal is not predictable in practice, the SSU must perform a continuous monitoring. To perform this continuous monitoring “sentry” function, existing SSU's must employ all of their resources (typically an amplifier, ADC, followed by a DSP) to collect and analyze data. Typical ADC-DSP chips consume 10's of milliwatts of power (depending on the sample rate and computations performed) which severely limits battery life. For example, in vibration sensing one typically collects data at a 10 kHz sample rate and performs a spectral analysis of the data which requires about 50 milliwatts of power for the ADC-DSP; this would consume the charge of a Li-Ion battery of 1 cubic inch volume in about 1 week. A typical compromise is to employ the sensor with a limited duty cycle to preserve energy. When a change in status is detected one may proceed to take a more detailed “look” to assess and respond appropriately. However, in many applications such as surveillance, monitoring of structures etc. this reduced duty cycle monitoring is not a viable option, and continuous monitoring becomes necessary.
If the total signal power generated from the sensing element is of interest, the estimation algorithm and the required circuitry are rather simple. However, in applications such as machine monitoring, the signal of interest normally appears within a narrow bandwidth, surrounded by wide-band noise and/or strong interfering signals. Thus, the SSU must distinguish signals propagating within environmental noise and interference, i.e., the SSU must extract the spectrum of the signal in a noisy environment. The system shown in FIG. 2 could achieve this type of narrow band power estimation by using an analog bandpass filter at the analog front-end followed by the ADC and simple digital processing algorithms such as summation of the squared values from the ADC. However, passive narrow-band analog filters require complex networks of components and are inflexible. Active (switched capacitor) narrow band analog filters are expensive in terms of power consumption. Thus, these options would not be acceptable in the majority of applications.
As an alternative to analog front-end filters, the DSP unit could perform more complex operations, such as digital filtering. Although the power consumption of the SSU while performing narrow band power estimation can be minimized by optimizing the processing algorithm, this approach cannot radically lower the power consumption. As shown below, the other problem related to the power consumption issue is that even while performing power estimation, the ADC usually has to operate at full resolution to avoid the introduction of significant error. Thus, this approach will not achieve SSU energy efficiency.
What is needed is a sentry circuit configured to monitor a sensor output before employing conventional energy inefficient processing such that a deployed sensor manages its resources in a power efficient manner.